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Found 9 papers, 1 papers with code
Markov Chain Mirror Descent On Data Federation
Stochastic optimization methods such as mirror descent have wide applications due to low computational cost.
Medical Federated Model with Mixture of Personalized and Sharing Components
Comparing with 13 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency.
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
To address this issue, we propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information to learn more informative node representations.
Understand Dynamic Regret with Switching Cost for Online Decision Making
We provide a new theoretical analysis framework, which shows an interesting observation, that is, the relation between the switching cost and the dynamic regret is different for settings of OA and OCO.
Simultaneous Clustering and Optimization for Evolving Datasets
Specifically, we propose a new variant of the alternating direction method of multipliers (ADMM) to solve this problem efficiently.
Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend
Decentralized Online Learning (online learning in decentralized networks) attracts more and more attention, since it is believed that Decentralized Online Learning can help the data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers.
Dynamic Online Gradient Descent with Improved Query Complexity: A Theoretical Revisit
We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment.
Proximal Online Gradient is Optimum for Dynamic Regret
While the online gradient method has been shown to be optimal for the static regret metric, the optimal algorithm for the dynamic regret remains unknown.
Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets
The reason is that it finds the similar instances according to their features directly, which is usually impacted by the imperfect data, and thus returns sub-optimal results.
